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Artificial intelligence, bias and clinical safety BMJ Quality and Safety Robert Challen, Joshua Denny, Martin Pitt, Luke Gompels, Tom Edwards, Krasimira Tsaneva-Atanasova January 2019 This article has received 680 academic citations, and had 26 citations in Policy Documents. See the full details below. Journal: Authors: Publication date:
Real world impact
Citation source Citation by source Top sources
Cited:
in 26 documents (Health Policy Documents) from 9 countries with 17 different sources
BMJ Impact Analytics April 2025
Citing policy and guidance countries
Citing country
Australia Intergovernmental Organization (IGO) EU New Zealand Spain UK Belgium Netherlands Sweden
Academic impact
Altmetrics April 2025 and Dimensions April 2025
Let’s hear from Robert Challen, University of Bristol, about this 2019 article pubished in BMJ Quality and Safety “...without open access you miss the opportunity of exposing your ideas to new communities with fresh perspectives.”
Robert Challen is based at the UKRI AI Hub for Collective Intelligence, Engineering Mathematics, University of Bristol; part of the University of Exeter Medical School; and the National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula (PenARC). What inspired you to pursue research in this particular area? I have a background in both clinical medicine and health information technology. Health IT has enabled a great deal of change in the delivery of clinical medicine but the complex interaction between system design and clinical workflow can inadvertently introduce risk. In 2019 the use of data driven machine learning to help make clinical decisions was in its infancy. Applying the learning from health IT implementation, and extending it to include the specific issues surrounding data driven decision support tools, is critical to patient safety. How do you envision your research contributing to the broader field or addressing real-world issues? AI in the workflow of clinicians and patients is almost an inevitability. AI tools are emerging from fields outside of healthcare where safety may not always be a primary consideration. We aimed to create a practical framework for AI developers, policy makers, or end users, to consider how AI in healthcare might fail, and how that might manifest.
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What do you see as the benefits of open access publishing compared to traditional publishing models? I am a strong believer in open source software as a way of sharing useful tools, but more importantly the ideas and techniques behind them. Open source software can be a foundation that can be iteratively improved on by collaboration of a broad community. Fundamentally, in my view, knowledge is no different. If you choose to make your knowledge accessible only to a select few then you limit the value you give to the community, and that you get back from the community. Providing open access was critical to expose these ideas of clinical safety in AI to as wide a community of developers, policy makers, and end users, as possible, many of whom would be outside of academic institutions with subscriptions to medical journals. What advice would you give to other researchers who are considering publishing their work in open access formats for the first time? For me the question is not why publish open access, but rather why not publish open access? There may be situations where the community of people who are interested in your work are small and affiliated with academic institutions. Even then without open access you miss the opportunity of exposing your ideas to new communities with fresh perspectives.
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“For me the question is not why publish open access, but rather why not publish open access?” Robert Challen, University of Bristol
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